Добірка наукової літератури з теми "Centralized statistical monitoring"

Wireless Sensor network (WSN) is an emerging technology widely applied in environmental disasters monitoring. With the constraint of computation resource, it face big challenge of stability and reliability of monitoring network. A statistical model of strain data and distributed monitoring algorithm for landslide based on WSN is studied in this paper. The strain data is modeled using variable mean of Gaussian process. Miss alarm rate and false alarm rate are introduced as critical performance parameters of landslide prediction. Comparing with centralized monitoring method, simulation result shows that distributed monitoring algorithm performs better than centralized monitoring.

On the train parts of 5T system at present all kinds of fault real-time monitoring the relevance of the weak, poor information sharing, centralized monitoring problem not in time, the system can achieve 5T effective integration of information system, information real-time delivery, 5T information fusion and 5T information fusion integrated query and statistical analysis and other functions through the acquisition of 5T system data, to train vehicle as the object of management, in order to train car number as the only connection identifier, fusion 5T information resources, to establish a global view of train vehicle safety monitoring information model. To improve the level of security early warning and fault analysis ability is of great significance.

Traffic calming devices on urban streets, such as elevated pedestrian crossings, speed bumps and roundabouts, are increasingly used, raising a real problem in relation to the on-board comfort that passengers perceive. To measure vibrational comfort related to traffic calming devices that passengers of the public transport perceive, an acquisition system called ASGCM (Autonomous System for Geo-referenced Comfort Measurements) has been developed, taking as a reference the European regulations on rail transports. ASGCM permits to link each measurement of vibration, ground velocity and acceleration with geographical information resulting from a GPS. In this way a map of a comfort index, statistical surveys and correlation between on-board comfort and traffic calming, can be directly obtained by any Geographic Information System (GIS), able to query a centralized remote database, which was developed ad- hoc. A large number of experimental tests has been performed to define a vibrational comfort index and to collect a large dataset that allows statistically significant comparisons between different infrastructures and their characterization. The proposed technique can also be useful for diagnostics purposes, such as vehicle comparison and road maintenance state monitoring.

Colonial administration, as every other administration, was built on the production and management of numbers: export figures (to assess the economic performance of each colony); population estimates, often falsely labeled “censuses” (to establish each colony's capacity to pay the head tax); school enrollment statistics (to establish budgets and document the road to “civilization”). French colonialism was probably one of the more centralized and number-producing systems. The regional (e.g., Dakar) and central (Paris) capitals were always requesting data for budgeting or simply for monitoring the evolution of each component of the empire.In the field of population statistics, before 1945 the process yielded very few reliable data, though a more systematic examination is required to be sure. Historically this can be explained by the evolution both of data collection and training in statistics in France during the first half of the twentieth century. The situation was well documented in the first decades of the century by Fernand Faure, a prominent member of the Société de Statistique de Paris, who noted that training in statistics was not very popular in the French civil service because no specific demand was made by higher levels of administrative or political power. Nevertheless, the Société and individuals in the Statistique Générale de France did succeed in pressing for the creation in 1922 of the Institut Supérieur de Statistique de l'Université de Paris (ISUP), but the lack of means at the institute made it virtually impossible for it to meet its training objectives.

In today’s technological and stressful world, when everyone is busy in their daily routines and places blind faith in pharmaceutical advancements to protect their health, the sudden, horrifying effects of the COVID-19 pandemic have resulted in serious emotional and psychological impacts in the general population. In spite of advanced vaccination campaigns, fear and hesitation have become a part of human life since there are a number of people who do not want to take these immunity boosting vaccinations. Such people may become carriers of infectious viruses, leading to a more rapid rate of spread; therefore, this class of spreaders needs to be screened at the earliest opportunity. In this context, there is a need for advanced health monitoring systems which can assist the pharmaceutical industry to monitor and record the health status of people. To address this need and reduce the uncertainty of the situation, this study has designed and tested an Internet of Things (IoT) and Fog computing-based multi-node architecture was for real-time initial screening and recording of such subjects. The proposed system was able to record current body temperature and location coordinates along with the facial images. Further, the proposed system was able to transmit data to a cloud database using internet-connected services. An implementation and reviews-based working environment analysis was conducted to determine the efficacy of the proposed system. It was observed from the statistical analysis that the proposed IoT Fog-enabled ecosystem could be utilized efficiently.

Physico-chemical and microbiological pollutants may compromise quality of drinking water. This study aims to highlight various physico-chemical and microbial parameters of drinking water samples from centralized water storage reservoirs of National University. Major water quality parameters which were examined in this study includes pH, Electrical conductivity (EC), Turbidity, Total suspended solids (TSS), Total dissolved solids (TDS), Dissolved oxygen (DO), Residual chlorine, Hardness, Alkalinity. Moreover, microbial analysis of water samples was also carried out through Most Probable Number technique (MPN). Results show that all the physico-chemical parameters were within prescribed limits of World Health Organization and Pakistan Drinking Water Quality Standards (WHO/PDWQS) in both underground and overhead water storage reservoirs except DO ( 9.05-9.2 and 9.05-9.1 mg/L ), TSS (10-20 and 3.3-10 mg/L) and residual chlorine (0.08-0.14 and 0.19-0.28 mg/L) respectively. The MPN index values for both underground and overhead water storage reservoirs ranges between 16- >23 and 1.1-12 respectively and it shows high microbial contamination in water due to low detection of residual chlorine. The results highlight that water from centralized water storage reservoirs of National University is unfit for drinking purposes. Statistical analysis such as paired t-test also reveals that water quality parameters from underground and overhead storage reservoirs are not significantly different from each other except EC and TDS having P values <0.05. It is necessary to safeguard water contamination in water storage reservoirs by monitoring regular inspections and chemical cleaning of water storage reservoirs otherwise it will cause serious threats to well-being of the community. Key words: water storage reservoirs, drinking water, microbial quality of water, physico-chemical parameters, paired t-test

PURPOSE: The goal of this study was to assess the impact of an interdisciplinary remote patient monitoring (RPM) program on clinical outcomes and acute care utilization in cancer patients with COVID-19. METHODS: This is a cross-sectional analysis following a prospective observational study performed at Mayo Clinic Cancer Center. Adult patients receiving cancer-directed therapy or in recent remission on active surveillance with polymerase chain reaction–confirmed SARS-CoV-2 infection between March 18 and July 31, 2020, were included. RPM was composed of in-home technology to assess symptoms and physiologic data with centralized nursing and physician oversight. RESULTS: During the study timeframe, 224 patients with cancer were diagnosed with COVID-19. Of the 187 patients (83%) initially managed in the outpatient setting, those who did not receive RPM were significantly more likely to experience hospitalization than those receiving RPM. Following balancing of patient characteristics by inverse propensity score weighting, rates of hospitalization for RPM and non-RPM patients were 2.8% and 13%, respectively, implying that the use of RPM was associated with a 78% relative risk reduction in hospital admission rate (95% CI, 54 to 102; P = .002). Furthermore, when hospitalized, these patients experienced a shorter length of stay and fewer prolonged hospitalizations, intensive care unit admissions, and deaths, although these trends did not reach statistical significance. CONCLUSION: The use of RPM and a centralized virtual care team was associated with a reduction in hospital admission rate and lower overall acute care resource utilization among cancer patients with COVID-19.

Crises are always challenging for banking systems. In the case of COVID-19, centralized payment networks and FinTech companies’ websites have been affected by user behavior globally. As a result, there is ample opportunity for marketing managers and professionals to focus on big data from FinTech websites. This can contribute to a better understanding of the variables impacting their brand name and how to manage risk during crisis periods. This research is divided into three stages. The first stage presents the web analytics and the data retrieved from the FinTech platforms. The second stage illustrates the statistical analysis and the fuzzy cognitive mapping (FCM) performed. In the final stage, an agent-based model is outlined in order to simulate and forecast a company’s brand name visibility and user behavior. The results of this study suggest that, during crises, centralized payment networks (CPNs) and FinTech companies with high organic traffic tend to convert new visitors to actual “customers”.

Our research object was long-term dynamics of parameters describing drinking water from centralized water supply systems and additional cases of health disorders among population in Russia directly caused by low-quality drinking water. Our research goal was to perform hygienic assessment of priority risk factors related to drinking water and potential health disorders that could be caused by them. Our research technique was hygienic analysis of drinking water parameters as per data taken from the federal statistical form No. 18 entitled «Data on sanitary situation in a RF region» over 2000–2019, the federal information fund of social and hygienic monitoring over 2012–2019, and calculation of associated health disorders as per MG 5.1.0095–14. Specific weight of centralized water supply sources that didn’t conform to sanitary-epidemiologic requirements decreased by 4.7 % over 2000–2019 and amounted to 14.9 %. Over the last 20 years there has been a descending trend in specific weight of water samples taken from centralized water supply sources that don’t conform to sanitary requirements as per sanitary-chemical parameters (by 2.7 %) and microbiological parameters (by 4.8%). Over 2000–2019, specific weight of water distribution networks not conforming to sanitary-epidemiologic requirements decreased by 10.7 % and amounted to 15.9 % in 2019. Over the last 20 years there has been an increase in quality of drinking water taken from centralized distribution networks. Specific weight of water samples from centralized distribution networks not conforming to sanitary requirements as per microbiological and sanitary-chemical parameters fell by 6.7 % and 7.9 % respectively. In 2012–2019 in the RF hygienic parameters of drinking water quality were the most violated as per contents of bromine, silicon, chlorine, iron, sodium, lithium, magnesium, manganese, chloroform, boron, strontium, sulfides, and hydrogen sulphide. Overall, in the RF in 2019 more than 1.66 million cases of digestive diseases, cardiovascular diseases, diseases of skin and subcutaneous tissue, and other health disorders were directly related to drinking water being contaminated with chemicals and microbiological agents; it was by 13.3 % lower than in 2012 regarding morbidity associated with drinking water quality. Priority risk factors were chlorine, chlorine organic compounds (COC), ammonia, iron, manganese, arsenic, nickel, copper, boron, magnesium, and other compounds.

La qualité des données est une préoccupation fondamentale de la recherche clinique. Pour garantir cette qualité, il faut pratiquer un monitorage continu des données. Les organismes internationaux de régulation des médicaments recommandent que ce monitorage soit ciblé, basé sur une analyse des risques. De cette recommandation a germé le concept de « monitoring statistique centralisé » (MSC) qui consiste à détecter des distributions de variables atypiques dans un centre par rapport aux autres centres. Cette thèse recense les méthodes de MSC existants, en propose de nouvelles, et compare les performances des unes et des autres. Dans la première partie, nous rappelons l’intérêt du sujet, dans un contexte marqué par l’accroissement du nombre d’essais cliniques, la nécessité de travailler de plus en plus à distance et le besoin de nouveaux paradigmes de monitorage. Dans la seconde partie, nous recensons les méthodes de MSC existantes, analysons leurs performances rapportées dans la littérature et en tirons deux observations majeurs : (i) le nombre de méthodes est limité; (ii) leurs évaluations par des travaux de simulations et des applications sur données réelles rapportées dans la littérature sont également limitées. Dans la troisième partie nous proposons deux nouvelles méthodes de MSC pour détecter les distributions de variables atypiques dans les essais multicentriques, l’une pour données quantitatives qui utilise une mesure de distance standardisée (méthode de la Distance) et l’autre pour données catégorielles, qui utilise un modèle Bayésien hiérarchique bêta-binomial (HBBB). Nous évaluons les performances de ces méthodes en utilisant des simulations d'essais cliniques, puis les comparons à d’autres méthodes de MSC identifiées dans la littérature. Pour les données quantitatives, la méthode de la Distance a des performances similaires à la méthode proposée par Desmet et al., et supérieures à celles des deux autres méthodes existantes. Pour les données catégorielles, la méthode HBBB a des performances similaires à la seule autre méthode existante, également proposée par Desmet et al. Pour les deux méthodes, Distance et HBBB, la sensibilité est globalement médiocre, mais la spécificité excellente, y compris dans de nombreux scénarios impliquant de petits effectifs. La sensibilité faible suggère que le MSC est un outil supplémentaire pouvant être utilisé en complément des autres procédures de monitoring conventionnelles, mais ne les remplace pas. La spécificité forte et le caractère convivial suggère que ces méthodes peuvent être appliquées en routine dans tous les essais cliniques, car leur utilisation ne prendra pas beaucoup de temps au niveau central et n'engendrera pas de charge de travail inutile dans les centres investigateurs
Data quality is a fundamental concern of clinical research. To ensure this quality, continuous data monitoring must be practiced. International drug regulatory bodies recommend that this monitoring be targeted, based on a risk analysis. From this recommendation emerged the concept of “centralized statistical monitoring” (CSM) which consists of detecting atypical distributions of variables in a center compared to other centers. This thesis identifies existing CSM methods, proposes new ones, and compares the performances of each. In the first part, we recall the interest of the subject, in a context marked by the increase in the number of clinical trials, the need to work increasingly remotely and the need for new monitoring paradigms. In the second part, we identify existing CSM methods, analyze their performances reported in the literature and draw two major observations: (i) the number of methods is limited; (ii) their assessments through simulation studies and applications on real data reported in the literature are also limited. In the third part we propose two new CSM methods to detect the distributions of atypical variables in multicenter trials, one for quantitative data which uses a standardized distance measure (Distance method) and the other for categorical data, which uses a hierarchical Bayesian beta-binomial (HBBB) model. We evaluate the performance of these methods using clinical trial simulations and then compare them to other CSM methods identified in the literature. For quantitative data, the Distance method has performances similar to the method proposed by Desmet et al., and superior to those of the two other existing methods. For categorical data, the HBBB method has similar performance to the only other existing method, also proposed by Desmet et al. For both methods, Distance and HBBB, the sensitivity is poor overall, but the specificity is excellent, including in many scenarios involving small sample sizes. The low sensitivity suggests that the CSM is an additional tool that can be used in addition to other conventional monitoring procedures, but does not replace them. The strong specificity and user-friendliness suggest that these methods can be routinely applied in all clinical trials, as their use will not be centrally time consuming and will not create unnecessary workload in investigational centers

Army Aviation is one of the heaviest data consumers of all service branches, and is heavily reliant upon consistent data streams. Currently, a complex web of information systems contains the various pieces of data, which hinders not only the use of the existing data systems, but also the development of cutting-edge data consumers. The AVX Aircraft Company, under the direction of the U.S. Army Combat Capabilities Development Command Aviation and Missile Center, assembled a team from industry and academia with uniquely complimentary skills to prototype and showcase a centralized data analysis space and toolset. Team members from the Applied Research Laboratory at Penn State University developed a cloud-hosted integration environment, granting secure and compliant access to a variety of developers and other interested parties. PeopleTec, Inc. curated a comprehensive dataset from six different H-60 Blackhawk helicopters, including health and usage monitoring data, maintenance records, and flight recorder data. Researchers from the Institute for Materials, Manufacturing, and Sustainment at Texas Tech University set to work developing statistical data combinations and reductions, as well as interactive and novel visualizations of the above data and results. The final cloud-hosted toolset demonstrated the overwhelming value of the additional analysis capabilities a centralized data access portal could present to Army Aviation. The ability to rapidly draw conclusions from the alignment, refinement, and display of what are currently disparate data sources represents a massive opportunity to enhance the capability and reliability of units across U.S. Army Aviation.

Abstract The K-W horizontal well was drilled to produce gas reserves from A sand and oil reserves from B sand. Accurate landing of the well in the target B sand is critical for the placement of the 6 1/8" lateral section within the reservoir. The main objective of reservoir navigation in the 8 ½" build section is to detect and map the reservoir top as early as possible to smoothly place the well 10ft TVD below the top of the B sand with an inclination of 85 degrees. Several operational risks have been identified during pre-well feasibility study, including reservoir resistivity and thickness variation, presence of intra-shale, presence of faults, and depth uncertainty from old seismic data. To mitigate the risks, extra-deep azimuthal resistivity technology was included in the bottom hole assembly. The real-time reservoir navigation was carried out remotely with frequent updates on real-time log analysis and observations provided to the client via a 24/7 virtual meeting. The service provider implemented a newly introduced centralized reservoir navigation system, which provides quicker data handling and seamless job handover. Multiple deterministic inversion workflows with constrained parameters were set-up to process the real-time extra-deep resistivity measurement, maximizing reservoir detection and mapping capability. As a result, the 8 ½" hole section of K-W well has been landed within the target formation. After the last marker, log signatures indicated good correlation with offset well data, extra-deep resistivity detected the presence of sand from a distance of approximately 34ft TVD. However, the automated inversion multi-layer mapping indicated possible reservoir pinch-out. A decision was made by the operator to further drop inclination and cut down through the entire formation. When the well crossed the expected reservoir top, extra-deep resistivity quickly decreased and log signatures indicated the presence of a thin sand before entering the basal shale formation, which was accurately mapped by inversions. In the post-well phase, deterministic uncertainty and the new statistical uncertainty analysis were run to evaluate the position of the main boundaries defined by extra-deep reading measurements. 1) deterministic approach is instantaneous and based on the linearized dependency between measurements and model parameters; 2) statistical quantification is based on the analysis of set of intermediate inversion models and includes industry-standard P50 visualization. This paper presents a case history of implementing extra-deep resistivity technology to overcome geological uncertainties during well landing in a complex environment. The novel approach of utilizing multiple deterministic inversion workflows maximized detection capability and accurate mapping of the reservoir structure, allowing early risk mitigation even before entering the target reservoir.

Abstract With increasing number of wells added over time in the matured fields to production sustainability, asset monitoring has become an integral part of the daily asset operation. Monitoring large number of wells as creates massive challenge related to increased man-hours spend and reduced efficiency due to manual work-process. This paper explains the data driven approach to overcome challenges monitoring for the asset operation utilizing the Integrated Asset Operation Model (IAOM) and Business Intelligence (BI) tools. Prior to the implementation of IAOM and BI tool, conventional manual work-process was performed to achieve the asset monitoring. The data driven approach of IAOM links different corporate data sources under a single centralized digital platform. This platform utilizes historical, real time and model-based data for further analysis, visualization, and monitoring purposes. IAOM utilizes a combination of data to run the complex engineering algorithm and presents analytical output to the users in the form of trends, grid, and statistical screen. The smart user defined BI dashboard and reports is integrated with the IAOM platform thus creating a single digital framework for asset monitoring. The key drawbacks of the conventional manual steps are the increasing man-hours on data gathering and data validation. Thus, resulting in less time on analysis and delaying bottleneck detection. After the implementation of data driven approach as part of IAOM, the increase man-hours diverted on analysis instead of data gathering and validation. The utilization of complex engineering algorithms and automated workflows have significantly reduced time consumption on asset monitoring and debottlenecking. The linkage of different data sources has enabled creating a single source of record (Sor) across the entire asset. This has also improved the efficiency and accuracy of the analytical studies. The intuitive plotting and trends functionality of the data or combination of data helps in comparative analysis over time or related well parameters. Integration of advanced BI tool within the IAOM has enabled creation of smart and intuitive dashboards and reports which cater to the needs of different job roles within the asset. Thus, the end user gets a bird eye view on the information relevant for his day-to-day asset monitoring task. As a part of IAOM, standard Thirteen workflows and work-process were implemented with an ability to consume large and varied data set from different sources for more accurate and efficient outcomes with respect to the conventional approach. This approach is an integration of data driven analysis, user expertise and advanced technology capitalization.

Lifestyle, lack of physical movement, sedentariness have all led to the growth of obesity among young people in our country. The major risk of obesity at a young age is that such persons would continue to have overweight issues later on in life, with all the negative consequences implied. The purpose of this research is: to measure and assess in a complex way, by using modern technology and an appropriate software program, the body composition of students at the Politehnica University of Bucharest, to create a database for the body composition of young people aged between 19 and 25 years, which is relevant by the number of subjects and the complexity of the assessed indicators, as well as to obtain real data allowing for the subsequent optimization of the means used in physical education and sports lessons. The sample investigated comprised 361 subjects, of which 207 males and 154 females, with ages between 19 and 25 years, all students at University Politehnica of Bucharest. The students participating in this research were informed about its objectives and agreed to the processing, analysis and storage of the data regarding their assessments. In this research we have used the Tanita FitScan BC-601 device with bodyvision software, considered by the specialists as being a powerful instrument in assessing not only the health condition in terms of body composition but also in the monitoring of efficiency of any fitness program. Using bioelectric impedance analysis technology (BIA) based on the data provided by 8 electrodes, the Tanita soft provided 19 assessments: 9 at a global level and 10 at a segmentar level (arms, legs and trunk). Of all these assessments, the most relevant for our research are the weight, BMI, total body fat, visceral fat, muscle mass, bone mass, metabolic age and total body water. The values provided by Tanita have been recorded in protocols, centralized in tables and statistically and mathematically processed. The comparison of the indicators obtained with the reference values according to the subjects' gender and age particulars has been presented graphically. The values resulting from this research have been capitalized in a database for the population with ages between 19 and 25 years according to the gender particulars for the following indicators: weight, BMI, total body fat, visceral fat, muscle mass, bone mass, metabolic age and total body water. The results will be used in elaborating lesson structures for the students at University Politehnica of Bucharest, together with an optimal component of the aerobe effort that will lead, in time, at the normalization of the body composition and, indirectly, at the improvement of the health condition.